PT Unknown
AU Guillermo Torres
   Jan Rodríguez Dueñas
   Sonia Baeza
   Antoni Rosell
   Carles Sanchez
   Debora Gil
TI Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images
BT 7th Workshop on Digital Image Processing for Medical and Automotive Industry  in the framework of  SYNASC 2023
PY 2023
AB This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.
ER